Method and Device for Monitoring Medication Usage

ABSTRACT

The present invention provides methods for detecting and quantifying metabolites in a biological sample by measuring the concentration of a test metabolite in the sample and comparing that concentration against the concentration of the reference metabolite; enabling accurate metabolite concentration measurements to determine aberrant drug usage patterns. Also disclosed is an analytical testing device and related computer-assisted products for detecting and quantifying metabolites in a biological sample efficiently and accurately.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.14/457,466 filed on Aug. 12, 2014, which is a continuation of U.S.application Ser. No. 12/843,662 filed on Jul. 26, 2010, which is acontinuation of U.S. application Ser. No. 12/534,503 filed on Aug. 3,2009 and issued as U.S. Pat. No. 7,785,895 on Aug. 31, 2010, which is acontinuation of U.S. application Ser. No. 10/924,105 filed on Aug. 23,2004 and issued as U.S. Pat. No. 7,585,680 on Sep. 8, 2009, which claimsthe benefit of U.S. Provisional Patent Application No. 60/499,129 filedon Aug. 28, 2003. Each of these applications is incorporated byreference herein in its entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

BACKGROUND OF THE INVENTION

It is important for clinicians to be informed of a patient'sinappropriate use of prescribed/nonprescribed medications and/or illicitdrugs to be able to properly manage their patient's care. As such,strict adherence to pharmacological dosage regimens is a prerequisite tothe success of most treatments, particularly in patients in drug abuseprograms or chronic pain programs. Although drug screening specimencollection procedures have been used to ensure specimen integrity,patients demonstrate considerable ingenuity in their efforts to defeatthe testing process (1-4). Methods used by patients for avoiding drugmisuse detection have included: diversion, excessive water consumption,ingestion of diuretics (e.g., herbal teas) and urine substitution.Individuals who divert pain medication will often “hold” a few pills tobe taken before a physician visit (the “white lab coat” effect). Thisallows the medication to show up in their system in order to ensure thatthe physician renews the prescription, thereby allowing them to continuediverting the medication.

Individuals will also overuse medications, often gaining it frommultiple sources. These individuals often pass the basic screening testsperformed at a clinic and continue to receive the medication.Furthermore, patients treated with narcotics for the management ofchronic pain also have been documented to under report their use ofmedications, especially for the opioid class of medications (5-11).Thus, to properly manage patient care, clinicians use external sourcesof information such as interviews with spouses, review of medicalrecords, input from prescription monitoring programs, and testing ofbiological samples (e.g., urine) to detect inappropriate use ofprescribed and nonprescribed medications, as well as, illicit drugs.

It has been previously reported that urine testing has the greatestpotential for determining true compliance (12). The major problem facingurine testing is the large amount of variance in urine drugconcentrations, mostly due to variations in hydration and urinary outputvolume (44, 45, 34). In a well-hydrated person, the level of drugmetabolites per milliliter may be quite small, while a poorly hydratedperson will show significantly higher levels of metabolites permilliliter. Studies have reported that if hydration is controlled, thedrug metabolite level will be more consistent and accurate for urinetesting. It has also been suggested that creatinine normalization may beused in correcting for variations in metabolite urine tests (13, 17, 35,36, 34, 40, 42, 44, 46); however, others have been skeptical of thispractice (41, 45).

Creatinine is a metabolite of creatine, and is an end-product of musclemetabolism excreted in the urine. Creatinine formation and excretion aredirectly proportional to total muscle mass and are roughly proportionalto body weight. Creatinine is excreted in relatively constant amounts of1.0 to 2.5 g/day regardless of urinary volume (14, 15). Alsonormalization of the excretion of a drug to the creatinine concentrationreduces the variability of analyte measurement attributed to urinedilution.

Use of creatinine to reduce variance due to dilution has been suggestedin the literature. However, most of these reports focus on illicit drugabuse where the concept of dose-specific use of the urine screening doesnot apply due to non-uniform doses and delivery systems of the illicitdrugs being studied, i.e., cocaine and marijuana (16) or have notattempted to develop a normative database with confidence intervals orregression models (42). Literature in this field has also pointed to theuse of urine creatinine as one method for possible adjustment that couldbe made in, for example, a one-step dilution protocol, but which mayproduce spurious results when urine creatinine is either extremely highor low (17).

Few, however, have attempted to employ use creatinine to adjust forhydration in order to monitor how a prescription drug is being used.Manno et al., reported that the Syva EMIT®-d.a.u. urine cannabinoidassay (Dade Behring, Palo Alto, Calif.) could be successfully used fordetecting marijuana use patterns in a urine surveillance program ifcreatinine was used to reduce variance due to hydration (26). It wasalso reported that a delta-9-tetrahydrocannabinol-9-carboxylic acid(THCA)/creatinine ratio should decrease over time when there is no newuse and a recommendation was made that when comparing results, theTHCA/creatinine ratio should decrease by 50% every 2-10 days dependingon the individual. More recently studies have confirmed the usefulnessof the THCA/creatinine ratio under controlled-dosing conditions withmarijuana smokers (37, 38). For example, it is believed that sequentialcreatinine normalized urine drug concentrations could predict whethermarijuana exposure had re-occurred or if the presence of THCA in urinewas due to continued clearance from the body in 85% of the cases.Reports have also shown that ratios in a light or infrequent user candecrease faster than in a heavy or frequent user. Some research hasshown the value of using creatinine when looking at urine testing forcotinine and the effects of passive smoke inhalation (43); however,others have remained skeptical about this benefit (41).

Still other studies have attempted to develop a urine drug screenprotocol by performing dose specific analyses that identified possibleimproper users, but have downplayed the importance of those findings(42). In these studies, the addition of a ratio of EDDP metabolite tomethadone dose and then dividing that by the urine creatinine (i.e.,[EDDP/methadone dose]/urine creatinine) may have disrupted the analysisand lessened the overall value of the correction. Urine creatinine hasbeen recommended for use in the fields of illicit drug use (e.g.,cocaine, marijuana, etc.). However, it has never been used to developdose-specific confidence intervals or regression analyses that wouldallow clinicians to verify patient medication use that is consistentwith proper use of the prescription through urine testing, with theexception of the above noted study that had the described flaws. To dateno researchers have attempted to develop a urine drug screen protocolfor abusable prescription medications that would enable dose specifictesting and allow identification of proper vs. improper use of themedication in question. Rather, to date, a test result is purelynegative or positive as to the presence or absence of a drug metabolitein the urine. Accordingly, it would be useful to develop a method toassess with confidence patient adherence to prescribed drug treatmentregimens.

BRIEF SUMMARY OF THE INVENTION

The present invention is summarized as a method for detecting andquantifying at least one metabolite in a biological sample having a testmetabolite. The method is carried out by contacting the biologicalsample with a device capable of distinguishing between the testmetabolite and a reference metabolite; detecting the presence of atleast one test metabolite in a biological sample; and quantifying theconcentration of at least one test metabolite in a biological sample bycomparing a ratio between a set of unknown data from the test metaboliteversus a set of known normative data specific to the referencemetabolite. The method of the present invention enables improvedclinical accuracy of protocols used in testing biological samples, suchas, urine testing.

One aspect of the present invention is to use the ratio between the testmetabolite and reference metabolite to identify non-adherence to aprescribed medication regimen.

Another aspect of the present invention is to develop dose relatednormal distribution curves or confidence intervals, allowing a physicianto quickly determine whether prescribed medication has been used in amanner consistent with the prescription.

Another aspect of the present invention provides an analytical devicefor detecting and quantifying the concentration at least one metabolitein a biological sample.

Another aspect of the present invention provides a test strip fordetecting and quantifying metabolites in a biological sample.

Another aspect of the present invention provides for computer-relatedproducts, suitably in the form of software and hardware for accuratelymonitoring prescription adherence by reducing the variability ofmetabolite concentrations due to dilution for improved clinical accuracyof urine testing protocols.

In this aspect, the invention provides for computer programs, internetand intranet products used to compare and statistically analyze, forexample, a new urine screen result with the normative data set to obtainimproved clinical accuracy.

Also in this aspect, the invention provides, that thesecomputer-assisted products are capable of interacting and updating thenormative database and statistical features of that database through a“self-correction” mechanism, such that when each new observation isadded into the database, the confidence intervals and regressionequations are automatically corrected with respect to the urinereference sample in the normative dataset and the specific attributes ofthe urine screening elements.

In yet another aspect, the invention provides a kit for monitoringnonadherence to a prescribed medication regimen, wherein at least one orseveral drugs may be detected simultaneously.

Other objects, features, and advantages of the invention will be readilyapparent from the following detailed description taken in conjunctionwith the figures.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a graph showing uncorrected urine EDDP levels for confirmeddaily methadone doses.

FIG. 2 is a graph showing uncorrected urine creatinine levels forconfirmed daily methadone doses.

FIG. 3 is a graph plotting out the log of EDDP in urine sample versusconfirmed total daily methadone dose with 95% prediction intervals.

FIG. 4 is a graph showing a plot of laboratory EDDP/creatinine ratiosversus prescribed total daily methadone doses revealing that knowninstances of nonadherance or unusual physiological factors affectingmethadone metabolism fell outside of the 95% prediction intervals forthe model and which were readily detected.

FIG. 5 depicts a flexible multi-level, vertical, one-sided test stripfor determining metabolite concentration in urine.

FIG. 6 depicts a flexible multi-level, vertical, two-sided test stripfor determining metabolite concentration in urine.

FIG. 7 depicts a flexible multi-level (horizontal) test strip fordetermining metabolite concentration in urine.

FIG. 8 depicts a hard multi-level test strip for determining urinarymetabolite concentration.

FIG. 9 is a graph showing uncorrected urine oxycodone (from OxyContin®and/or oxycodone) for confirmed OxyContin® and/or oxycodone medicationcombinations.

FIG. 10 is a graph showing creatinine corrected urine oxycodone forconfirmed OxyContin® and/or oxycodone medication combinations.

FIG. 11 is a regression equation with predicted versus observedmetabolit concentration for the model for the metabolite (oxycodone fromeither OxyContin®, oxycodone, or both combined) as a function of doseand creatinine.

Other advantages and a fuller appreciation of specific adaptations,modifications, and physical attributes will be gained upon anexamination of the following detailed description of the variousembodiments, taken in conjunction with the appended claims.

DETAILED DESCRIPTION OF THE INVENTION

The present invention is based on the observation that currentlyavailable protocols for testing biological samples, such as for example,urine are insufficient for determining accurate concentrations of commonmetabolites corresponding to prescribed drugs. The major difficulty inurine testing is the variability in concentrations of a metabolite ofinterest, depending on the level of hydration of an individual at thetime of testing. Accordingly, the present invention provides a novelmethod for accurate and efficient detection and quantification of atleast one metabolite, which is specific for a prescribed medication.This method is capable of taking into account hydration-based dilutionchanges by developing normal distribution curves or confidenceintervals. Thus, the present invention can substantially improve theability of a clinician to monitor and confirm whether a patient has beenusing the medication in a manner which is consistent with theprescription.

As such, the following is a more detailed description of the variousembodiments of the invention including definitions and examples.

In the present context the term “biological sample” refers to urine,blood, saliva, sweat, and spinal and brain fluids, or a combinationthereof.

Also as used herein, the term “test metabolite” is intended to indicatea substance the concentration of which in a biological sample is to bemeasured; the test metabolite is a substance that is a by-product of orcorresponds to a specific prescribed drug. The term “referencemetabolite” is intended to indicate a substance used to calibrate ornormalize the test metabolite against which the test metabolite ismeasured to determine test metabolite concentration levels; it is also asubstance that corresponds to a specific prescribed drug.

The term “prescribed drug” refers to a variety of common drugs,including but not limited to common opioids, common stimulants, andcommon benzodiazepines. It is generally known that the drugs may havemultiple combinations of common urinary metabolites and differentmethods for measuring the metabolites. Table 1 provides a non-limitinglist of common drugs and the corresponding readily quantifiablemetabolites in biological samples, such as urine.

TABLE I COMMON DRUGS COMMON METABOLITES COMMON OPIOIDS OxyContin ®oxycodone MS Contin morphine Kadian morphine Avinza morphine codeinecodeine and morphine methadone EDDP Duragesic Patch fentanyl DarvocetN-100 propoxyphene hydrocodone hydrocodone COMMON STIMULANTSRitalin/methypehnidate amphetamine Dexedrine/dextroamphetamineamphetamine COMMON BENZODIAZEPINES Valium/diazepam diazepam/temazepamRestoril/temazepam temazepam/diazepam Xanax/alprazolamalphahydroxyalprazolam Ativan/lorazepam lorazepam

The term “drug metabolite/creatinine ratio” refers to the ratio that iscomprised of the amount of a specific metabolite of a specific drugfound in a urine sample as measured by a specific test, usually statedin ng/ml divided by the amount of urine creatinine found in the urinesample as measured by the assay of urine creatinine, usually stated indL/ml.

Applicants envision that although urine creatinine has been identifiedas one method of adjusting for hydration, and the method of choice atthis time, other methods and metabolites may become available to allowimprovement in the adjustment for hydration, which could lead toimprovement in the overall predictive ability. The present applicationencompasses those methods and metabolites that may become available toenable improvement in the adjustment for hydration.

The term “regression equation” refers to a technical statisticalprocedure by which two or more variables are shown to be consistentlyrelated that allows one or more variables to predict the correspondingvalue of the other variable and is one procedure that allows thesepredictions to occur.

The term “test strips” or “analytical test strips” refers to an in vitrodiagnostic tool designed to detect the presence of drugs of abuse withparticular sensitivity and clarity. It is intended for use in researchand educational institutions, hospitals, clinical laboratories,substance abuse clinics, law enforcement agencies, and for individualsor companies doing drug screening programs.

The term “normative database” refers to the concept of a collected setof data that is related to the specific population it is intended topredict. Statistical analysis of such a set of data, enables a person ofordinary skill in the art to perform predictive analysis based on thenorms of the data set under the basic assumptions set forth bystatistical analysis.

The term “self-correction” refers to the concept where each newobservation (or piece of data) is subsequently added into the normativedatabase discussed herein above as it occurs, which leads to improvementof the norms from that database. This leads to a normative database andstatistical analyses that more consistently describe the true populationit is intended to describe and ultimately predict.

Generally, in practicing the present invention, there are severaldifferent embodiments that can be used to determine the presence andconcentration of at least one or more metabolites in a biologicalsample. For example, a metabolite concentration can be derived by use ofa test strip which is capable of being flexible so as to be submergedreadily into a biological sample. The test strips may be eitherone-sided or multi-sided and vertical in nature, as in FIG. 5 orhorizontal in nature, as in FIG. 6. It is envisioned that the test stripmay also be housed in a hard plastic case with an opening forintroduction of a biological sample such as a “drop” of urine and arectangular opening for display of the results (e.g., see SunLine® invitro urine drug screening test strips, described in U.S. Pat. Nos.5,238,652; 6,046,058; 5,962,336; and 6,372,516). However, in contrast tothe SunLine® test, the test strips of the present invention have amulti-threshold and multi-level arrangement as is shown in FIG. 8.

It is also envisioned that the test strips of the invention may beprepared in the conventional manner as described in U.S. Pat. Nos.6,210,971 and 5,733,787 to Bayer Corporation (Elkhart, Ind.)(incorporated herein in its entirety). For purposes of this invention, atest strip may be characterized as an absorbent substrate capable ofimmobilizing metabolites bound to a layer of support material.Well-known solid phase supports may include paper, cellulose, fabricsmade of synthetic resin, e.g. nylon or unwoven fabric. The absorbentmaterial is typically bound to a layer of support material such as glassfiber or a synthetic polymer sheet to provide structural support. Thoseskilled in the art of analytical test devices are aware of many othersuitable solid phase supports for binding metabolites or are able toascertain the same by use of routine experimentation.

In another embodiment the present invention may be useful for clinicianswho practice in rural areas. For example, a slide-rule may be developedfrom normative data for measuring the concentration level of a type ofprescribed drug.

In yet another embodiment, the invention provides analytical testdevices, such as test strips in portable kits for determining metaboliteconcentrations at multiple levels using non-invasive and visuallydetectable methods. In this embodiment, the kit may include ananalytical device for measuring the metabolite concentrations atmultiple levels, as well as instructions for use of the kit components.

Other embodiments of the above and present invention are furtherillustrated by the following examples.

EXAMPLES Example 1 Monitoring of Methadone Treatment

This example describes how a drug metabolite/urine creatinine ratio inpatients on chronic methadone therapy (either for pain or for opioidaddiction) could be used to improve the ability of clinicians to predictappropriate use of prescribed medication, as well as detect and quantifyinappropriate use. Accordingly, a regression model with narrowconfidence intervals and high coefficients of determination over aclinically significant range of prescribed dosages was established. Thisregression model was used to compare predicted urine metabolite levelsfor a prescribed regimen against actual metabolite levels revealingnonadherence to a given regimen to improve monitoring of medicationusage.

Subjects

The charts of patients receiving methadone treatment for eithersubstance abuse or chronic pain over a three month time period werereviewed. For inclusion in the study, the records were required tocontain age, height and weight data, a specified methadone dosingregimen, and urine samples that included quantitative urine creatinineand EDDP: 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidone (methadonemetabolite) levels. Prior to chart abstraction of qualifying patients,individual patient consent was obtained according to InstitutionalReview Board protocols. Any patient not agreeing to allow their recordsto be reviewed for this project was excluded from analysis. Forconsenting patients, in addition to the information listed above, anyclinical notes relevant to patient drug regimen adherence or conditionsexpected to effect methadone metabolism were recorded.

Urine Collection

Weekly random urine screens to reveal common drugs of abuse wererequired of all methadone maintenance patients. All chronic painpatients signed an opioid agreement that required random urine screensfor: 1) the level of the drug they were prescribed; 2) any other opioidmedications; and 3) any street or illicit drug.

Urine was collected following a standard protocol for random urinescreening. Clinic drug abuse or chronic pain patients were provided 30mL specimen cups with temperature-sensitive strips. If urine temperaturewas identified to be appropriate for a “fresh” urine specimen, it wastransferred to the laboratory for determination of a specific urinecreatinine level and a general “Drug of Abuse” (also referred to as aprescribed drug) test that included EDDP quantitation via GC-MSanalysis.

Creatinine Determinations

Creatinine levels were determined employing the Jaffe reaction (DRI®Creatinine-Detect; Microgenics Corp., Fremont, Calif.), whereby a redcreatinine-picrate complex is formed (40). The rate of formation of thecolor product is measured using bichromatic pairing (absorbance at 505nm minus nonspecific absorbance at 570 nm). Samples werecolorimetrically analyzed on a Hitachi 717 analyzer (Hitachi ChemicalDiagnostics, Inc., Mountain View, Calif.). The reaction rate was used toconstruct a linear standard curve from which the concentration ofcreatinine is calculated.

EDDP Determinations

Aliquots of the urine specimens are first screened for drugs of abuse bya routine immunoassay, such as ELISA, known in the art. Each presumptivepositive specimen was tested as a confirmation. In the case ofmethadone, EDDP was quantitatively measured by gas chromatography/massspectrometry (GC-MS) with selected ion monitoring (SIM) (18-21). Forcalibration, EDDP was spiked into certified negative urine at aconcentration of 300 ng/ml. For test samples, an internal standard(D3-EDDP) was added, the urine was alkalinized by addition of strongbase, and EDDP was extracted with organic solvent. Because EDDP has nopolar functional groups, derivatization was not required. The organicextract was dried under N₂, reconstituted, and analyzed. The fragmentions that were monitored are as follows:

TABLE II Metabolite Quantitating Ion Qualifying Ion EDDP: 277 276 262D3-EDDP: 280 265

The internal standard makes a small contribution to the analyte ionchromatograms, especially to the 262 ion. This limits the sensitivity ofthe assay to a 10 ng/ml level of detection.

Use of Urine Creatinine to Adjust for Hydration

EDDP/creatinine ratios were calculated by dividing the EDDP level(ng/ml) by the urine creatinine level (mg/dL).

Statistics

Regression analyses were used to model EDDP and the EDDP/creatinineratio from valid urine screening results as functions of methadone dose,body size, gender, and age. All analyses were conducted in a repeatedmeasures [mixed linear model (24)] framework to allow for correlationamong multiple observations from the same patient. Backward selectionwas used to remove non-significant terms from the model when consideringadditional factors, and the coefficient of determination (R²) wasreported as a measure of model fit. Plots of the data and analyses ofthe residuals and predicted values from the regression models were usedto ensure that the final models adequately represented the observeddata, and 95% prediction limits (see, J. Neter and W. Wasserman; AppliedLinear Statistical Models, Homewood, Ill.: Richard D. Irwin (1974),which discusses confidence limits for a single new observation) werecalculated. Results in this report were deemed statistically significantat the 5% level (p<0.05). Body surface areas (BSA) were calculatedaccording to the Mosteller formula (23, 24):

BSA(m²)=([height(cm)×weight(kg)]/3600)^(1/2)

-   -   Lean body weights (LBW) were calculated as follows (24):

LBW(men)=(1.10×weight(kg))−128×(weight²/(100×height(m))²)

LBW(women)=(1.07×weight(kg))−148×(weight²/(100×height(m))²)

Patient Characteristics

Eight patients were identified that met the aforementioned inclusioncriteria. One of those chose not to participate. The remaining subjectswere either patients on methadone maintenance for opioid addiction (3subjects, all females) or chronic pain patients who were on methadonefor pain control (4 subjects, 3 males and 1 female). The specificcharacteristics of each of the patients used in this study is providedin TABLE III shown herein below

TABLE III Type of Urine Sub- Treatment Dose Range per Screen Daily DoseOther Patient ject Age Program day Collections Manner of CollectionsConfirmation Characteristics 1 27/28 year Methadone 102.5 to 120 mg 10 8occurring on a day after an observed dose Yes Experienced a old femaleMaintenance and 2 occurring at the end of her miscarriage pregnancy thatlikely resulting in substantial changes in ended in metabolism,producing an aberrant result miscarriage while on 120 mg daily 2 25 yearMethadone 102.5 to 120 mg 9 8 occurring following an observed dose and 1Yes old female Maintenance occurring following reported hoarding ofmedication on the day prior to specimen collection producing an aberrantresult 3 29 year Methadone 60 mg 9 5 occurring on a day after anobserved dose, 4 Yes old female Maintenance occurring on a day after shedid not take methadone the previous day 4 34 year Chronic pain 15 to 60mg 3 A total of 3 urine screens met criteria, 1 No (patient old femalepatient occurred after 5 mg every 8 hours (15 mg reported taking totaldaily; 1 occurred after 10 mg every 12 the medication as hours (20 mgtotal daily); and 1 occurred after prescribed) 30 mg every 12 hours (60mg total daily) 5 41 year Chronic pain 10 to 45 mg 2 1 occurred after 5mg every 12 hours (10 mg No (patient old female patient total daily) and1 occurred after 15 mg every 8 reported taking hours (45 mg total daily)the medication as prescribed) 6 51 year Chronic pain 15 mg every 6 1 No(patient old male patient hrs (60 mg total reported taking daily) themedication as prescribed) 7 49 year Chronic pain 15 mg every 1 No(patient old male patient morning and 5 reported taking mg every eveningthe medication as (20 mg total prescribed) daily)

Data Subset

For establishment of a regression model for the prediction of methadoneintake from selected clinical parameters, data known to be aberrant wereexcluded from initial analysis.

Regression Analysis

Uncorrected urinary EDDP metabolite and creatinine concentrations werequite variable and did not correlate well with known methadone doses asillustrated in FIGS. 1 and 2.

For establishment of a regression model for the prediction of methadoneintake from selected clinical parameters, the five urine screens assumedto be aberrant were excluded from the initial analysis leaving 30 (81%)available for modeling (two observations when patient was miscarryingwere excluded from all analyses). Uncorrected urinary EDDP metaboliteand creatinine concentrations were quite variable and did not correlatewell with known methadone doses (FIGS. 1 and 2). When a regression modelwas formed with the log of EDDP, there was a significant relationship(p<0.001) as shown in FIG. 3.

Although the relationship was significant, the regression line explainedless than 60% of the variability (R²=0.58), and the log analysis did notdifferentiate compliant individuals who had been taking the medicationon the day previous to testing from those who either did not take themedication the day previous to testing or those who had some problemwith metabolism at the time of testing.

A substantially better predictive model was obtained for theEDDP/creatinine ratio. A quadratic model(EDDP/Creatinine=5.48+0.98*Dose+0.0056*Dose²) fit the observed datasignificantly better than a linear model and provided an R²=0.97 (FIG.4). This means that 97% of the original variability was explained by themodel, which was highly statistically significant (p<0.001).

Sensitivity to Known Outliers

The goal of the model was to be able to predict whether a patient hadadhered to a prescribed dosage regimen. Plotting the laboratorydetermined EDDP/creatinine ratios from the known aberrant data againstthe prescribed (expected) methadone dose showed that these data felloutside of the 95% prediction limits for the quadratic model (FIG. 4).While the regression equation above produced excellent results, wesought to determine whether the predictive value of the model could beimproved by considering patient characteristics that might affect theratio. In that creatinine formation is directly proportional to totalmuscle mass, we considered age, body size (height, weight, BSA, BMI, andLBW), and gender, all of which factor into a person's muscle mass. Noneof these factors significantly improved the fitted model; although, withonly seven patients in the analysis, our ability to assess anyindependent contribution of these factors was limited.

The results set forth above, clearly demonstrate that the use of a urinecreatinine ratio when analyzing methadone metabolites in urine,substantially reduced the variability associated with the urine testing.Multivariate analysis revealed that any interindividual variations inmuscle mass due to age, gender, height and weight, BMI or LBW areadequately represented by the urine creatinine levels only. No furthercorrection for these factors was found to be of any benefit. The use ofa urine creatinine ratio when analyzing methadone metabolites in urineexplained a statistically significant and substantial proportion of thevariability associated with the urine testing results. Aberrantmethadone use or metabolism in the methadone maintenance patients wasreadily apparent. If this model is further validated, it couldsubstantially benefit methadone maintenance programs by reducing theneed for observer confirmation of doses and improving reliability inmonitoring. A higher level of verifiable control may thereby beachieved. This model could substantially benefit methadone maintenanceprograms by reducing the need for observer confirmation of doses andimproving monitoring reliability.

The results of the methadone study further verify previous literaturereports that expressing urinary drug metabolite concentration as a ratioto the amount of creatinine can adequately control for urine volumefluctuations. The American Conference of Governmental IndustrialHygienists recommend that urine creatinine ranging between 20 and 350mg/dL are valid (25). Normalizing urine drug concentrations to urinecreatinine values has been attempted for drugs such as marijuana(26-29), amphetamine (30), cocaine (31), nicotine (32) and buprenorphine(33). Most of these applications, however, have been designed with thespecific aim of avoiding false negative results in drug screeningprograms due to very dilute urine specimens (28, 34, 35).

The data used in this study was sufficient to yield a highly significantregression that allowed the demonstration of known outliers.Intra-individual variability in renal excretion of creatinine can betemporarily increased by meat consumption (14, 15, 39). This variabilitymay account for 10%-29% between-day variation in calculated creatinineclearance for a given individual (16). These parameters were notconsidered in this study, but are expected to be considered in futurestudies.

CONCLUSIONS

Accordingly, it is envisioned that the methadone screening protocol ofthe present invention has the potential to enable providers to identifyaberrant use patterns in patients (e.g., over or under using themedication). Additionally, urine metabolite/urine creatinine ratio couldbe applied to any drug that can be tested through urine, includingopioids to provide for enhanced understanding of how a patient is takingthat drug. This would be especially advantageous for medications thatare taken on a timed basis (i.e., OxyContin®) and other drugs that havea high potential for misuse, abuse, or diversion due to high streetvalue. Thus, by correcting fluctuations in urine dilution throughmetabolite/creatinine ratio determinations, the potential exists for theability to test and monitor for patient methadone adherence across arange of clinically relevant dosages. It is expected that a similarapproach may prove useful for other drug treatments as well.

To provide a fuller understanding of the scope of the present invention,the following prophetic examples are provided.

Example 2 Overuse of Opioid Medication

In this example, a patient in a methadone maintenance program wasdirected by the clinician to take 100 mg per day of methadone and wastaking an observed daily dose of that amount. On Tuesday he arrived forhis daily observed medicine and a urine screen was performed. The EDDP(methadone metabolite) was present in his urine at an extremely highlevel (e.g., 35000 ng/ml), which may or may not be consistent with hisprescribed dose because hydration affects the amount of the metabolitefound per ng/ml. The EDDP level in urine is highly variable and is notspecifically correlated to dose of drug. His creatinine level (e.g., 20mg/dL) indicated that he was quite well-hydrated. The drug metabolitewas divided by creatinine level to produce the metabolite/creatinineratio (e.g., 1750). This drug metabolite/creatinine ratio was found tocorrelate well with a drug-dose and a confidence interval for creatininethat is approximate to the dose of drug that would be appropriate forthat ratio. This information was compared to normative tables and thisamount was consistent with a daily methadone dose of 500 mg, anindication that this patient was gaining methadone from another sourceand using substantially larger amounts of methadone than has beenprescribed at the methadone clinic. The drug metabolite/creatinine ratiohas been helpful in identifying an aberrant use pattern for the aboveopioid addict. Under previous testing conditions, this patient wouldhave been continued on that medication because he passed thepositive-negative urine test. However, by using urine metabolite/urinecreatinine ratio coupled with appropriate normative data, an approximatedose range based on the urine test was developed, which allowed theclinician to easily identify the aberrant use patterns. Accordingly,this novel analytical method enables the clinician to make a moreaccurate assessment of the situation before discontinuing theprescription.

Example 3 Underuse of Opioid Medication

In this example, a patient in a chronic pain program has been prescribedOxyContin® 60 mg every 12 hours, for a total daily dose of 120 mg. Thepatient arrives at the pain clinic for his regularly scheduled follow-upvisit and a random urine screen is performed. The patient's urine screenreveals the drug metabolite oxycodone at a level of 1000 ng/ml and acreatinine level of 80 mg/dL. Generally because the OxyContin®metabolite was present in the urine during the urine screen thepatient's medication would have been refilled and continued because thepresence of the drug metabolite by itself has been shown to not becorrelated to drug dose. The presence of the metabolite in the urine hassimply been correlated with the fact that the patient has been takingthe drug. However, by using the drug metabolite/creatinine ratio equalto 12.5, which is far too low for the drug dose in question. A review ofnormative data with this ratio suggests that the patient would have tobe taking less than 10 mg of OxyContin® on a daily basis (or recently)to achieve that ratio level. This suggests that the patient has not beentaking the medication in the way it has been prescribed and has eitherbeen 1) diverting the medication and had taken just a small dose of themedicine prior to the office visit to “pass” the positive/negative urinescreen or 2) had been overtaking the medication (i.e., abusing themedicine) but had effectively run out of the medication and had saved 1or 2 pills to take prior to the urine screen in order to “pass” theurine screen. Therefore, by using the method of the present invention,the patient's aberrant use pattern for OxyContin® was identified.

Example 4 Use of Multi-Level Urine Test Strips for Opioids

In this embodiment of the present invention, urine test strips may beused as an alternative to the standard GC-MS testing to detect andmeasure opioid levels in urine. It is envisioned that the urine stripswould have detectable markings ranging from, for example, level 1 tolevel 10 for differentiating between multiple concentrations ofprescribed drugs. It is expected that a one-sided multi-level urinestrip, for example, depicted in FIG. 5, could be used where a patienthas been prescribed 60 mg of OxyContin® to be taken every 12 hours, fora total daily dose of 120 mg. The patient which is in a chronic painprogram then arrives at the pain clinic for a regularly scheduledfollow-up visit at which a random urine screen is performed. The urinescreen is carried out by submerging a one-sided flexible test strip inthe patient's urine sample for a predetermined amount of time. Theclinician may then visually observes the concentration of metabolite ina patient's system by reading the test strip.

In this case, the patient's urine screen reveals the drug metaboliteoxycodone at a level greater than 1000 ng/ml but less than 1500 ng/ml(i.e., Level 3 on the oxycodone strip). When combined with a creatininelevel via a creatinine test strip, which verifies a creatinine levelgreater than 70 mg/dL and less than 90 mg/dL (i.e., Level 6 on thecreatinine strip). The clinician then is able to calculate a metabolite(level 3)/creatinine (level 6) ratio and with the aid of normativetables, quickly is able to identify that the patient's urinequantification does not match the prescribed drug dose, indicatingaberrant use. These multi-level urine test strips are advantageous overthe current “one threshold” model strips currently on the market thatare used for general positive/negative drug testing, which simplydetects the presence or absence of a drug at the predetermined thresholdlevel. Thus, if a clinician were to use the current “one threshold”strips in this case, the patient's medication would be incorrectlyrefilled and continued.

In another embodiment of the invention, depicted in FIG. 6, it isexpected that a two-sided multi-level urine strip, would be suitable ina situation, where a clinician would like to test for more than one drugmetabolites simultaneously using the same strip. In this scenario, theclinician would submerge the two sided flexible test strip in abiological sample (e.g., a urine sample, for a predetermined amount oftime. The clinician may then visually observe the concentration ofmetabolite in a patient's system by reading the test strip. Applicantsalso envision that a multi-sided, multi-level urine test device (i.e.,three-sided or more) may be useful in a situation where the clinician isinterested in testing for aberrant use of a series of drugs.

Furthermore, it is envisioned that with the normative data developed fora number of different drugs, several multi-level urine test strips maybe used simultaneously, in combination with each other to achievemaximum dose accuracy, to identify inappropriate drug use. Specifically,by using normative data a Level 3 oxycodone strip combined with a Level6 creatinine strip would be most consistent with aberrant use of theprescribed medication. Presumably, the aberrant use would be the resultof either 1) diverting the medication and taking just a small dose ofthe medicine prior to the office visit to “pass” the positive/negativeurine screen; a result of 2) overtaking the prescribed medication (i.e.,abusing the medicine) or a situation where the patient had effectivelyrun out of the medication but had saved 1 or 2 pills to take in order to“pass” the urine screen. Thus, using the multi-level urine strips, thepatient's aberrant use pattern would be readily identified by theclinician.

Example 5 Overuse of Benzodiazepines

In yet another example of the present invention, it is encompassed thata psychiatrist could prescribed Xanax® 1 mg prn up to tid for a patientto manage her chronic anxiety. The patient arrives at her normallyscheduled follow-up visit and reports that she has been taking hermedicine exactly as prescribed. She indicates that she has taken 1 mgtid for each of the last 3 days due to her intense anxiety. She deniesever taking more than three pills per day. She would like an increase inher dosage to help her with her intense anxiety. She is sent for arandom urine screen and it reveals that her level of benzodiazepinemetabolite for alprazolam is 5000 ng/ml. This would normally beconsidered appropriate and her prescription would be refilled. However,when creatinine is used to correct for hydration (i.e., 50 mg/dL), it isobserved that the ratio from normative information is indicative of aperson who is taking a dose between 2 mg tid and 2.5 mg tid. Thepsychiatrist discusses this with the patient and she admits that she hasbeen getting Xanax® from multiple sources and feels that she is“addicted” to the medication. She is referred for inpatient drugtreatment. Accordingly, under current testing conditions, because thepatient passed the positive-negative urine test, the psychiatrist wouldhave continued prescribing the same dosage or an increased dosage of themedication. However, by using urine metabolite/urine creatinine ratiowith appropriate normative data, an approximate dose range based on theurine test was developed and was capable of easily identifying theaberrant use patterns of the patient.

Example 6 Overuse of Stimulants Identified by Multi-Level Urine TestStrips

In still another example of the present invention, it is encompassedthat an adult male could be prescribed Ritalin® 10 mg tid (30 mg totaldaily) for management of Adult ADHD. A regularly scheduled urine testingis performed using multi-level urine test strips. The commonly observedRitalin urinary metabolite is shown to be present in his system at aLevel 6 (between a range of 3000 ng/ml and 3500 ng/ml), and creatinineis found to be present at Level 2 (between range of 20 ng/ml and 30ng/ml). The normative values associated with this combination oftest-strips is indicative of Ritalin® usage of between 60 mg total perday to 80 mg total per day, suggesting the patient is substantiallyoverusing the medication. A pharmacy check is performed and reveals thatthis patient has been receiving Ritalin® from 3 different providers forseveral months. The patient is confronted with this information andappropriate treatment is started. Under current drug testing conditions,because this patient would have passed the positive-negative urine test,and would have continued to receive prescriptions for Ritalin®. Thus, byusing drug metabolite/urine creatinine ratio with appropriate normativedata, an approximate dose range based on the urine test was developedand easily identified the aberrant use patterns.

Example 7 Computer-Assisted Products Using Normative Data

In another embodiment of the present invention, it is envisioned thatone skilled in the art would be able to enter patient-specific data(e.g., height, weight, age, sex, medication type, reported doses, GC-MSderived urine creatinine level, and drug metabolite urine level) into acomputer-assisted product, such as a software program or a related database; where the data would be compared to normative data for purposes ofidentifying aberrant drug use patterns in patients.

In one scenario, it could be expected that a patient, who is onlong-term opioid medication for chronic low back pain attends a regularfollow-up visit with her clinician and indicates that her medication(Kadian® 100 mg per 12 hrs) is not working well and she would like anincreased dosage. She is 5′2″ tall and weighs 140 pounds. A urine screenis performed utilizing GC-MS evaluation. The metabolite of Kadian®(i.e., morphine) is present in her system, suggesting the patient hasbeen taking at least some of the medication. Creatinine is also measuredas part of the urine screen. All of her relevant patient information(i.e., age, sex, height, weight, urine morphine level, urine creatininelevel, and number of reported Kadian® doses taken over the past twodays—4 doses at 100 mg) are input into the computer program or relateddatabase. The patient information is then analyzed and compared againsta metabolite specific normative database that graphically plots apatient's actual data versus the normative data with confidenceintervals via regression statistical analysis. From this graph it can beobserved that the patient's level is inconsistent with her report of 4doses over the past 48 hours. Her dose is suggestive of substantiallymore medication taken over the time period. Although, her pills werecounted and were consistent with her prescriptions within the painclinic, a pharmacy check identified that the patient has been receivingMS Contin® 75 mg per every 8 hours from another source and had likelybeen taking it in combination with the Kadian® 100 mg every 12 hours.Despite GC-MS testing capabilities that allow metabolite concentrationmeasurements in the nanogram per milliliter levels, current urinetesting methods still are unable to identify various concentrations ofmetabolites to determine aberrant drug usage patterns in patients.

Example 8 Other Computer-Assisted Products

In yet another embodiment of the present invention, a scenario isenvisioned where one skilled in the art would be able to determine withaccuracy the level of aberrant drug usage patterns in a patient who hasbeen on long-term transdermal opioid medication (Duragesic Patch 200 mcgper 3 days). The patient has had previous clean urine screens but heappears to be slightly early for his regular refill. A urine screen isperformed, with the addition of a kidney function test, as well as askin permeability analysis. All of the data (e.g., age, sex, weight,height, overall muscle mass, estimate of proportion of carbohydrates toproteins consumed during prior 48 hours, which may affect creatininelevel, dose of drug prior 2 days, last patch change, kidney functiontest, skin permeability analysis, urine creatinine level, and drugmetabolite level are input in the computer program. The program thenutilizes normative data and regression statistical methods toapproximate drug dosage used in the recent past. It was determined thatthis patient had not been taking his medication as prescribed. Rather,the computer-generated analysis results for this patient reveal that inthe past 48-hours a sudden large dose of the medication had been takenthat is inconsistent with the transdermal route of administrationadmitted to by the patient. The patient had likely in the previous48-hour period tampered with the patch delivery system and taken themedication through the oral route, generally referred to as, “suckingthe ooze”, which is known to lead to a substantial “high” on the street.Accordingly, this type of drug misuse would not have been identifiedwith the current analytical tests on the market today. However, inaccordance with the present invention the use of urine creatinine tocorrect for hydration, coupled with additional patient characteristicswhich allow for the variability in the regression analysis to bereduced, enables the identification of aberrant drug use throughcomputer-assisted products with statistical analysis capabilitiesconnected to a normative data base.

Example 9 Use of Slide-Rule or Dial Methods to Produce ConfidenceIntervals for Opioid Monitoring and/or Other Drug Monitoring

In another embodiment of the present invention, a scenario is envisionedwhere one skilled in the art would be able to determine with accuracythe level of aberrant drug usage patterns in a patient who is beingmedicated for a combination of chronic pain and adult ADHD by a ruralphysician. Given that the physician does not have the access to a drugscreening laboratory, he utilizes multi-level urine test strips fortesting of various medications. He has all his patients go through astandard protocol that first identifies a positive analysis of themedications and street drugs they have in their system. The abovepatient is positive for morphine and amphetamines, which is consistentwith his prescriptions. The next set of test strips identifies that heis at a level 7 for morphine; level 10 for amphetamines; and level 4 forcreatinine. Using a slide-rule developed from normative data for thetype of morphine this patient is on (i.e., MS Contin®), the patient'slevel is consistent with his prescribed dosage. Using a slide-ruledeveloped from normative data for the type of amphetamine this patientis on (i.e., Adderall XR®) the patient's level is suggestive of usageslightly higher than the prescribed amount. This is reviewed with thepatient who admits that he had been feeling “drowsy” from the MS Contin®and had been taking a few extra doses of Adderall XR® to get throughwork. Discussion ensues and a decision is made to transition the patientinto a different opioid that, hopefully, would not lead to the sameamount of drowsiness as the MS Contin®. Patient is happy with theresult. Again, the above scenario would not be possible with currenturine testing protocols but with appropriate adjustment for hydrationand normative data, the multi-level testing strips for the drugmetabolite(s) and urine creatinine, combined with the slide rule toassess normative data enables quick, inexpensive and informed medicaldetermination for a physician in a rural setting that does not haveaccess to a fully functioning urine testing facility.

Example 10 Use of Monitoring Methods in Methadone Maintenance Programs

Currently, individuals with severe opioid addiction are often placed ona methadone program maintenance for anywhere between 4 monthspost-detoxification to the rest of their lifetime, to assist them inavoiding a relapse to their drug use habits. All methadone maintenanceprograms require daily observed doses, with occasional take-home dosesfor one weekend day or on holidays. Accordingly, the foregoing exampledescribes the benefits of this novel testing methodology to reduce theneed for daily observed doses.

In one scenario, a female patient on 100 mg of methadone per day for amethadone maintenance program was provided with a one-week supply ofmedicine. She would be called to submit a random urine screen. Arequirement for continuing in the methadone maintenance program, onweekly take-home dosing, was that she would submit a urine sample within12 hours of the phone call requesting that sample. She submits hersample as requested and her urine level for EDDP and creatinine areanalyzed. The data obtained from the urine screen was compared tonormative metabolite data for methadone wherein the patient was on aonce daily dosing schedule. It is determined that the patient's drugmetabolite/urine creatinine ratio is consistent with a dose between30-50 mg of once daily dosing, indicating that this patient has beenunderusing her methadone on a daily basis. The comparative resultsobtained from the normative metabolite data allow the clinician toaccurately identify dose dependent levels of patient drug use thatcurrent testing methods are incapable of determining. Applicants believethat the use of this new methodology may substantially change thelandscape of methadone maintenance and allow for substantially reducedhealth care cost associated with daily observed dosing. Thus, undercurrent methodology, the patient who tests positive for the presence ofthe urinary metabolite would have continued receiving daily-observeddoses of methadone, despite her aberrant use patterns (i.e. “hoarding”medicine to use in large quantities or diverting it, as describedhereinabove).

Example 11 Use of Computer Program, Internet Web-Based Calculator, orIntranet Calculator

In another embodiment, the invention provides a computer program,internet web-based calculator or an intranet calculator that would allowa medical provider, without direct access to computer software, toutilize the normative database, confidence intervals, and/or regressionequations to improve accuracy in assessing results of a specific urineanalysis. In this scenario, a medical provider would access a web-siteor an intranet of a sponsoring clinic that would allow entry ofmeaningful data (e.g., metabolite levels, age, weight, creatinine level,etc.) and would provide confidence interval or regression equationoutcome. It is encompassed within the scope of this invention that oneskilled in the area could further develop a computer program orconversion algorithm that would allow for conversion of one type ofurine testing protocol or analysis to another type within anorganization (e.g., clinic) or between multiple organizations (e.g.,several clinics).

Example 12 Use of Computer Program, Internet Web-Based Calculator, orIntranet Calculator with Self-Correction

In another embodiment, the invention provides a computer program,internet web-based calculator or an intranet calculator that would allowfor data to be added into a normative database to improve the accuracyof the database and subsequent confidence intervals and/or regressionequations. Currently, normative databases are developed from specificresearch projects that are limited to a specific number of observations,with appropriate controls placed on each observation. These controls areplaced to ensure that the observations are consistent with the overallpopulation they are meant to represent. With the development ofappropriate technology and the participation of a large group of medicalproviders, the need for those controls would diminish because the studypopulation would actually be the same as the actual population. Thiswould thereby provide a true normative dataset that would enhance ourconfidence intervals and regression equations. With the addition of a“self-correcting” component in the database, meaning each newobservation added would become part of the database and further adjustthe database elements (e.g., regression equation, confidence intervals,means, standard deviations, error, etc.), the database would become aself-adjusting database. This allows the database to become morerepresentative of the actual population as time progresses. This wouldlead to improved accuracy in the predictions, confidence intervals, andregression equations that come from that dataset.

Example 13 Use of Covariates to Develop a Regression Model withOxyContin® and Oxycodone that can Later be Used for Prediction of DoseConfidence Interval

This embodiment of the invention provides for a retrospective studyperformed as described in Example 1 above, without the observed doses.It is noted that this study utilized retrospective data following thesame appropriate research analyses found in Example 1 with IRB approval.In this example, there were 27 valid observations for modeling. Thirteenobservations are dropped due to one or more of the following reasons: 1)metabolite result outside the limits; 2) non-compliance; and/or 3) oneof the conditions hepatitis, impaired kidney function, or amputee. Inaddition, two patients (IDs 231 and 235) with four observations wereexcluded from modeling due to documented non-compliance.

FIG. 9 and FIG. 10 show the raw data for the metabolite vs. dose and themetabolite/creatinine ratio vs. dose, respectively. A model formetabolite vs. the dose (both on log scales) with the urine creatinineas a covariate is substantially better than a model using themetabolite/creatinine ratio as the response (R²=0.78 vs. about 0.64,respectively). Both dose and creatinine are highly significant in themetabolite model (p<0.001).

Furthermore, FIG. 11 shows a plot of predicted vs. observed for themodel for the metabolite as a function of dose and creatinine. Withregard to covariates in the OxyContin® and Oxycodone model, gender andweight provide no real contribution to the model for the metabolite as afunction of dose and creatinine. Age shows some marginal association(p=0.062), but the adjusted R² decreases from 0.78 to 0.76.

Applicants note that this example displays the potential benefit of thismodel even when creatinine is a co-variant and not a primary element inthe regression model. In this model, similar to Example 1, it isenvisioned that with sufficient numbers of observations it will bepossible to properly assess via statistical analysis whether any oneobservation is within the appropriate confidence intervals as noted bythe regression model and use of identified co-variants.

Although the foregoing invention has been described in some detail byway of illustration and example for purposes of clarity ofunderstanding, it will be readily apparent to those of ordinary skill inthe art in light of the teachings of this invention that certain changesand modifications may be made thereto without departing from the spiritor scope of the appended claims.

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We claim:
 1. A method of determining a drug dose taken by a subject, themethod comprising: (a) determining a concentration of a test metaboliteand a reference metabolite in a biological sample obtained from asubject, wherein the test metabolite is the drug or a metabolite of thedrug; (b) determining the normalized concentration of the testmetabolite in the biological sample as a ratio of the concentration ofthe test metabolite to the concentration of the reference metabolite;and (c) determining the drug dose taken by the subject by using adatabase to determine the drug dose that is correlated with thenormalized concentration of the test metabolite obtained in step (b),wherein the database comprises (i) dose of the drug taken by a referencepopulation; (ii) reference metabolite concentration in a medium ofbiological sampling obtained from the reference population, and (iii)test metabolite concentration in the medium of biological samplingobtained from the reference population.
 2. The method of claim 1,wherein the biological sample and the medium of biological sampling areselected from the group consisting of: urine, blood, saliva, sweat,spinal fluid, brain fluid, and combinations thereof.
 3. The method ofclaim 1, wherein the reference metabolite is creatinine.
 4. The methodof claim 1, wherein concentration of the reference metabolite isdetermined by contacting the biological sample with a device capable ofdistinguishing between the test metabolite and the reference metabolite.5. The method of claim 5, wherein concentration of the referencemetabolite is determined using gas chromatography, tandem gaschromatography-mass spectroscopy, or a test strip.
 6. The method ofclaim 1, wherein prior to step (a), the subject has been prescribed adrug therapy regimen, drug dosing therapy, medication regimen, or hasbeen enrolled in a drug treatment program.
 7. The method of claim 1,wherein the database is used to develop a regression model correlating agiven drug dosage with the other variables included in the database, andwherein step (c) is performed using the regression model.
 8. The methodof claim 7, wherein the regression model defines one or moredosage-specific confidence intervals.
 9. The method of claim 1, furthercomprising monitoring the subject for possible non-adherence to aprescribed medication regimen by comparing the determined drug dose tothe dosage of the drug that is prescribed for the subject.
 10. Aninterindividual method of determining a drug dose taken by a subject,the method comprising: determining a concentration of a test metaboliteand a concentration of a reference metabolite in a biological sampleobtained from a subject, wherein the test metabolite is the drug or ametabolite of the drug; determining a normalized concentration of thetest metabolite in the biological sample as a ratio of the concentrationof the test metabolite to the concentration of the reference metabolite;and using the normalized concentration of the test metabolite todetermine the drug dose taken by the subject, without accounting for anyvariations between individuals other than reference metaboliteconcentration.
 11. The method of claim 10, wherein the biological sampleis urine and the reference metabolite is creatinine.
 12. The method ofclaim 10, wherein the drug is selected from the group consisting ofopioids, stimulants, benzodiazepines, and combinations thereof.
 13. Themethod of claim 10, wherein the drug is selected from the groupconsisting of oxycodone, morphine, codeine, methadone, fentanyl,propoxyphene, hydrocodone, methylphenidate, dextroamphetamine, diazepam,temazepam, alprazolam, lorazepam, buprenorphine, and combinationsthereof.
 14. The method of claim 10, wherein the test metabolite isselected from the group consisting of a drug, an opioid, a stimulant, abenzodiazepine, oxycodone, morphine, codeine, methadone, fentanyl,propoxyphene, hydrocodone, methylphenidate, dextroamphetamine, diazepam,temazepam, alprazolam, lorazepam, buprenorphine, metabolites thereof,and combinations thereof.
 15. The method of claim 10, wherein theconcentration of the reference metabolite is determined by contactingthe biological sample with a device capable of distinguishing betweenthe test metabolite and the reference metabolite.
 16. The method ofclaim 10, whereby the step of determining the normalized concentrationof the test metabolite in the biological sample adjusts theconcentration for changes in the subject's hydration status.
 17. Amethod of monitoring possible non-adherence to a prescribed medicationregimen comprising the steps of: (a) determining a concentration of areference metabolite and a concentration of a test metabolite in abiological sample from a patient receiving a prescribed medicationregimen; (b) determining a normalized test metabolite concentration thatis adjusted for changes in hydration status of the patient as a ratio ofthe test metabolite concentration to reference metabolite concentrationin the biological sample; (c) using a database to determine a testmetabolite/reference metabolite ratio that is correlated with theprescription medication dosage of the prescribed medication regimen,wherein the database comprises (i) prescription medication dosageadministered to a reference population that is receiving the prescribedmedication regimen; (ii) test metabolite concentration specific to amedium of biological sampling of the reference population, and (iii)reference metabolite concentration specific to a medium of biologicalsampling of the reference population, wherein a mismatch between thetest metabolite/reference metabolite ratio that is correlated with theprescription medication dosage of the prescribed medication regimen asdetermined in step (c) and the normalized test metabolite concentrationdetermined in step (b) indicates non-adherence to the prescribedmedication regimen.
 18. The method of claim 17, wherein the biologicalsample and the medium of biological sampling of the reference populationare both urine, and the reference metabolite is creatinine.
 19. Themethod of claim 18, wherein the database is used to develop a regressionmodel correlating a given drug dosage with the other variables includedin the database, and wherein step (c) is performed using the regressionmodel.
 20. The method of claim 19, wherein the regression model definesone or more dosage-specific confidence intervals.